SoroushVahidi/scidocs huggingface dataset:
Action understanding and active inference
(2011)

We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action–observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference.
Planning optimal grasps
(1992)

In this paper we will address the problem of planning optimal grasps. Two general optimality criteria, that consider the total finger force and the maximum finger force will be introduced and discussed. Moreover their formalization, using various metrics on a space of generalized forces, will be detailed. The geometric interpretation of the two criteria will lead to an efficient planning algorithm. An example of its use in a robotic environment equipped with two-jaw and three-jaw grippers will also be shown.
Non-Uniform Sinusoidally Modulated Half-Mode Leaky-Wave Lines for Near-Field Focusing Pattern Synthesis
(2015)

A novel non-uniform sinusoidally modulated half-mode microstrip structure with application to near-field focused leaky-wave radiation in the backward Fresnel zone is proposed. First, it is presented a dispersion analysis of the constituent backward leaky wave in the sinusoidally modulated unit cell in half-width microstrip technology. This information is then used to design a finite non-uniform line that focuses the radiated fields at the desired point. Finally, eight similar line sources are arranged in a radial array to generate a three-dimensional focused spot located at the desired focal length over the simple central coaxial feeding. Simulated and experimental results are presented to validate the proposed simple approach.
mPHASiS: Mobile patient healthcare and sensor information system
(2011)

Pervasive care and chronic disease management to reduce institutionalization is a priority for most western countries. The realization of next generation ubiquitous and pervasive healthcare systems will be a challenging task, as these systems are likely to involve a complex structure. Such systems will consist of various devices, ranging from resource-constrained sensors and actuators to complex multimedia devices, supporting time critical applications. This is further compounded by cultural and socio-economical factors that must be addressed for next generation healthcare systems to be widely diffused and used. In this study, the requirements for a vital sign monitoring solution space is derived and mPHASiS is developed based on these requirements. mPHASiS is an end to end solution not only providing sensor networking and vital sign monitoring but also closing the loop by signaling alert messages to the caregiver and allowing pervasive access to vital signs of a patient using smartphones over a heterogeneous network. A role based access control mechanism is developed to limit access to sensitive data. The end to end delay and delay variations for both vital sign data collection and pervasive access are analyzed. mPHASiS is developed as a complementary solution augmenting functionality of a hospital information system and can be loosely couple with the hospital information system using webservices. & 2010 Elsevier Ltd. All rights reserved.
An Integrated Experimental Environment for Distributed Systems and Networks
(2002)

Three experimental environments traditionally support network and distributed systems research: network emulators, network simulators, and live networks. The continued use of multiple approaches highlights both the value and inadequacy of each. Netbed, a descendant of Emulab, provides an experimentation facility that integrates these approaches, allowing researchers to configure and access networks composed of emulated, simulated, and wide-area nodes and links. Netbed's primary goals are ease of use, control, and realism, achieved through consistent use of virtualization and abstraction.By providing operating system-like services, such as resource allocation and scheduling, and by virtualizing heterogeneous resources, Netbed acts as a virtual machine for network experimentation. This paper presents Netbed's overall design and implementation and demonstrates its ability to improve experimental automation and efficiency. These, in turn, lead to new methods of experimentation, including automated parameter-space studies within emulation and straightforward comparisons of simulated, emulated, and wide-area scenarios.
Security measures in automation systems-a practice-oriented approach
(2005)

Often security is seen as an add-on service for automation systems that frequently conflicts with other goals such as efficient transmission or resource limitations. This article goes for a practice-oriented approach to security in automation systems. It analysis common threats to automation systems and automation networks in particular, sets up a model to classify systems with respect to security and discusses common measures available at different system levels. The description of measures should allow to rate the effects on the overall system security
Learner Engagement Measurement and Classification in 1:1 Learning
(2014)

We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data as Engaged or NotEngaged while observing videos of the students and their screens. Based on the collected data, perceptual user features (e.g., body posture, facial points, and gaze) are extracted. We use feature selection and classification methods to produce classifiers that can detect whether a student is engaged or not. Accuracies of up to 85-95% are achieved on the collected dataset. We believe our work pioneers in the successful classification of student engagement based on perceptual user features in a 1:1 authentic learning setting.
Low-Cost Gas Sensors Produced by the Graphite Line-Patterning Technique Applied to Monitoring Banana Ripeness
(2011)

A low-cost sensor array system for banana ripeness monitoring is presented. The sensors are constructed by employing a graphite line-patterning technique (LPT) to print interdigitated graphite electrodes on tracing paper and then coating the printed area with a thin film of polyaniline (PANI) by in-situ polymerization as the gas-sensitive layer. The PANI layers were used for the detection of volatile organic compounds (VOCs), including ethylene, emitted during ripening. The influence of the various acid dopants, hydrochloric acid (HCl), methanesulfonic acid (MSA), p-toluenesulfonic acid (TSA) and camphorsulfonic acid (CSA), on the electrical properties of the thin film of PANI adsorbed on the electrodes was also studied. The extent of doping of the films was investigated by UV-Vis absorption spectroscopy and tests showed that the type of dopant plays an important role in the performance of these low-cost sensors. The array of three sensors, without the PANI-HCl sensor, was able to produce a distinct pattern of signals, taken as a signature (fingerprint) that can be used to characterize bananas ripeness.
Cellular and molecular mechanisms of hepatocellular carcinoma: an update
(2012)

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor that accounts for ~80 % of all liver cancer cases worldwide. It is a multifactorial disease caused by a variety of risk factors and often develops in the background of underlying cirrhosis. A number of cellular phenomena, such as tumor microenvironment, inflammation, oxidative stress, and hypoxia act in concert with various molecular events to facilitate tumor initiation, progression, and metastasis. The emergence of microRNAs and molecular-targeted therapies adds a new dimension in our efforts to combat this deadly disease. Intense research in this multitude of areas has led to significant progress in our understanding of cellular processes and molecular mechanisms that occur during multistage events that lead to hepatocarcinogenesis. In this review, we discuss the current knowledge of HCC, focusing mainly on advances that have occurred during the past 5 years and on the development of novel therapeutics for liver cancer.
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment
(2017)

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious.
Personality and motivations associated with Facebook use
(2009)

Facebook is quickly becoming one of the most popular tools for social communication. However, Facebook is somewhat different from other Social Networking Sites as it demonstrates an offline-to-online trend; that is, the majority of Facebook Friends are met offline and then added later. The present research investigated how the Five-Factor Model of personality relates to Facebook use. Despite some expected trends regarding Extraversion and Openness to Experience, results indicated that personality factors were not as influential as previous literature would suggest. The results also indicated that a motivation to communicate was influential in terms of Facebook use. It is suggested that different motivations may be influential in the decision to use tools such as Facebook, especially when individual functions of Facebook are being considered. Ó 2008 Elsevier Ltd. All rights reserved. 1. Personality correlates and competency factors associated
Personality and the prediction of consequential outcomes.
(2006)

Personality has consequences. Measures of personality have contemporaneous and predictive relations to a variety of important outcomes. Using the Big Five factors as heuristics for organizing the research literature, numerous consequential relations are identified. Personality dispositions are associated with happiness, physical and psychological health, spirituality, and identity at an individual level; associated with the quality of relationships with peers, family, and romantic others at an interpersonal level; and associated with occupational choice, satisfaction, and performance, as well as community involvement, criminal activity, and political ideology at a social institutional level.
Rhythms of social interaction: messaging within a massive online network
(2007)

We have analyzed the fully-anonymized headers of 362 million messages exchanged by 4.2 million users of Facebook, an online social network of college students, during a 26 month interval. The data reveal a number of strong daily and weekly regularities which provide insights into the time use of college students and their social lives, including seasonal variations. We also examined how factors such as school affiliation and informal online “friend” lists affect the observed behavior and temporal patterns. Finally, we show that Facebook users appear to be clustered by school with respect to their temporal messaging patterns.
Match makers and deal breakers: analyses of assortative mating in newlywed couples.
(2004)

We conducted a comprehensive analysis of assortative mating (i.e., the similarity between wives and husbands on a given characteristic) in a newlywed sample. These newlyweds showed (a) strong similarity in age, religiousness, and political orientation; (b) moderate similarity in education and verbal intelligence; (c) modest similarity in values; and (d) little similarity in matrix reasoning, self- and spouse-rated personality, emotional experience and expression, and attachment. Further analyses established that similarity was not simply due to background variables such as age and education and reflected initial assortment (i.e., similarity at the time of marriage) rather than convergence (i.e., increasing similarity with time). Finally, marital satisfaction primarily was a function of the rater's own traits and showed little relation to spousal similarity.
INTRINSIC AND EXTRINSIC MOTIVATION 1
(2003)

A central tenet of economics is that individuals respond to incentives. For psychologists and sociologists, by contrast, rewards and punishments are often counterproductive, because they undermine “intrinsic motivation”. We reconcile these two views, showing how performance incentives offered by an informed principal (manager, teacher, parent) can adversely impact an agent’s (worker, child) perception of the task, or of his own abilities. Incentives are then only weak reinforcers in the short run, and negative reinforcers in the long run. We also study the effects of empowerment, help and excuses on motivation, as well as situations of ego-bashing reflecting a battle for dominance within a relationship.
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
(2018)

We present Deep Voice 3, a fully-convolutional attention-based neural textto-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training an order of magnitude faster. We scale Deep Voice 3 to dataset sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on a single GPU server.
Taking spiritual history in clinical practice: a systematic review of instruments.
(2013)

BACKGROUND
To facilitate the addressing of spirituality in clinical practice, several authors have created instruments for obtaining a spiritual history. However, in only a few studies have authors compared these instruments. The aim of this study was to compare the most commonly used instruments for taking a spiritual history in a clinical setting.


METHODS
A systematic review of spiritual history assessment was conducted in five stages: identification of instruments used in the literature (databases searching); relevant articles from title and initial abstract review; exclusion and Inclusion criteria; full text retrieval and final analysis of each instrument.


RESULTS
A total of 2,641 articles were retrieved and after the analysis, 25 instruments were included. The authors independently evaluated each instrument on 16 different aspects. The instruments with the greatest scores in the final analysis were FICA, SPIRITual History, FAITH, HOPE, and the Royal College of Psychiatrists. Concerning all 25 instruments, 20 of 25 inquire about the influence of spirituality on a person's life and 17 address religious coping. Nevertheless, only four inquire about medical practices not allowed, six deal with terminal events, nine have mnemonics to facilitate their use, and five were validated.


CONCLUSIONS
FICA, SPIRITual History, FAITH, HOPE, and Royal College of Psychiatrists scored higher in our analysis. The use of each instrument must be individualized, according to the professional reality, time available, patient profile, and settings.
A Metadata Catalog Service for Data Intensive Applications
(2002)

Advances in computational, storage and network technologies as well as middle ware such as the Globus Toolkit allow scientists to expand the sophistication and scope of data-intensive applications. These applications produce and analyze terabytes and petabytes of data that are distributed in millions of files or objects. To manage these large data sets efficiently, metadata or descriptive information about the data needs to be managed. There are various types of metadata, and it is likely that a range of metadata services will exist in Grid environments that are specialized for particular types of metadata cataloguing and discovery. In this paper, we present the design of a Metadata Catalog Service (MCS) that provides a mechanism for storing and accessing descriptive metadata and allows users to query for data items based on desired attributes. We describe our experience in using the MCS with several applications and present a scalability study of the service.
Using JFLAP to interact with theorems in automata theory
(1999)

An automata theory course can be taught in an interactive, hands-on manner using a computer. At Duke we have been using the software tool JFLAP to provide interaction and feedback in CPS 140, our automata theory course. JFLAP is a tool for designing and running nondeterministic versions of finite automata, pushdown automata, and Turing machines. Recently, we have enhanced JFLAP to allow one to study the proofs of several theorems that focus on conversions of languages, from one form to another, such as converting an NFA to a DFA and then to a minimum state DFA. In addition, our enhancements combined with other tools allow one to interactively study LL and LR parsing methods.
Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals
(2008)

The electromyographic (EMG) signal observed at the surface of the skin is the sum of thousands of small potentials generated in the muscle fiber. There are many approaches to analyzing EMG signals with spectral techniques. In this study, the short time Fourier Transform (STFT) and wavelet transform (WT) were applied to EMG signals and coefficients were obtained. In these studies, MATLAB 7.01 program was used. According to obtained results, it was determined that WT is more useful than STFT in the fields of eliminating of resolution problem and providing of changeable resolution during analyze.
Obesity is associated with macrophage accumulation in adipose tissue.
(2003)

Obesity alters adipose tissue metabolic and endocrine function and leads to an increased release of fatty acids, hormones, and proinflammatory molecules that contribute to obesity associated complications. To further characterize the changes that occur in adipose tissue with increasing adiposity, we profiled transcript expression in perigonadal adipose tissue from groups of mice in which adiposity varied due to sex, diet, and the obesity-related mutations agouti (Ay) and obese (Lepob). We found that the expression of 1,304 transcripts correlated significantly with body mass. Of the 100 most significantly correlated genes, 30% encoded proteins that are characteristic of macrophages and are positively correlated with body mass. Immunohistochemical analysis of perigonadal, perirenal, mesenteric, and subcutaneous adipose tissue revealed that the percentage of cells expressing the macrophage marker F4/80 (F4/80+) was significantly and positively correlated with both adipocyte size and body mass. Similar relationships were found in human subcutaneous adipose tissue stained for the macrophage antigen CD68. Bone marrow transplant studies and quantitation of macrophage number in adipose tissue from macrophage-deficient (Csf1op/op) mice suggest that these F4/80+ cells are CSF-1 dependent, bone marrow-derived adipose tissue macrophages. Expression analysis of macrophage and nonmacrophage cell populations isolated from adipose tissue demonstrates that adipose tissue macrophages are responsible for almost all adipose tissue TNF-alpha expression and significant amounts of iNOS and IL-6 expression. Adipose tissue macrophage numbers increase in obesity and participate in inflammatory pathways that are activated in adipose tissues of obese individuals.
A New Prime and Probe Cache Side-Channel Attack for Cloud Computing
(2015)

Cloud computing is considered one of the most dominant paradigms in the Information Technology (IT) industry nowadays. It supports multi-tenancy to fulfil future increasing demands for accessing and using resources provisioned over the Internet. However, multi-tenancy in cloud computing has unique vulnerabilities such as clients' co-residence and virtual machine physical co-residency. Physical co-residency of virtual machines can facilitate attackers with an ability to interfere with another virtual machine running on the same physical machine due to an insufficient logical isolation. In the worst scenario, attackers can exfiltrate sensitive information of victims on the same physical machine by using hardware side-channels. There are various types of side-channels attacks, which are classified according to hardware medium they target and exploit, for instance, cache side-channel attacks. CPU caches are one of the most hardware devices targeted by adversaries because it has high-rate interactions and sharing between processes. This paper presents a new Prime and Probe cache side-channel attack, which can prime physical addresses. These addresses are translated form virtual addresses used by a virtual machine. Then, time is measured to access these addresses and it will be varied according to where the data is located. If it is in the CPU cache, the time will be less than in the main memory. The attack was implemented in a server machine comparable to cloud environment servers. The results show that the attack needs less effort and time than other types and is easy to be launched.